Real Time System for Facial Analysis

Tutkimustuotos: Konferenssiesitys, posteri tai abstrakti ›

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Real Time System for Facial Analysis. / Tommola, Janne; Ghazi, Pedram; Adhikari, Bishwo ; Huttunen, Heikki.

2018.

Tutkimustuotos: Konferenssiesitys, posteri tai abstrakti ›

Bibtex - Lataa

@conference{483df6b8b95a419d9c390408354f322f,

title = "Real Time System for Facial Analysis",

abstract = "In this paper we describe the anatomy of a real-time facial analysis system. The system recognizes the age, gender and facial expression from users in appearing in front of the camera. All components are based on convolutional neural networks, whose accuracy we study on commonly used training and evaluation sets. A key contribution of the work is the description of the interplay between processing threads for frame grabbing, face detection and the three types of recognition. The python code for executing the system uses common libraries--keras/tensorflow, opencv and dlib--and is available for download.",

author = "Janne Tommola and Pedram Ghazi and Bishwo Adhikari and Heikki Huttunen",

year = "2018",

month = "11",

language = "English",

}

RIS (suitable for import to EndNote) - Lataa

TY - CONF

T1 - Real Time System for Facial Analysis

AU - Tommola, Janne

AU - Ghazi, Pedram

AU - Adhikari, Bishwo

AU - Huttunen, Heikki

PY - 2018/11

Y1 - 2018/11

N2 - In this paper we describe the anatomy of a real-time facial analysis system. The system recognizes the age, gender and facial expression from users in appearing in front of the camera. All components are based on convolutional neural networks, whose accuracy we study on commonly used training and evaluation sets. A key contribution of the work is the description of the interplay between processing threads for frame grabbing, face detection and the three types of recognition. The python code for executing the system uses common libraries--keras/tensorflow, opencv and dlib--and is available for download.

AB - In this paper we describe the anatomy of a real-time facial analysis system. The system recognizes the age, gender and facial expression from users in appearing in front of the camera. All components are based on convolutional neural networks, whose accuracy we study on commonly used training and evaluation sets. A key contribution of the work is the description of the interplay between processing threads for frame grabbing, face detection and the three types of recognition. The python code for executing the system uses common libraries--keras/tensorflow, opencv and dlib--and is available for download.

M3 - Paper, poster or abstract

ER -

TUTCRIS